Deception detection is typically utilized after the fact within the forensic phase of an investigation, rather than as a preventative method in real-time. Since humans are only able to discern between truthful and deceptive statements with very low accuracy, automated deception detection capabilities are vital due to the increasing reliance on online data sources.

This research combines BDI (Beliefs, Desires, Intentions) agents with a neural network module that provides the system with machine learning capability. JACK agents performed tasks automatically and autonomously. This study used these characteristics to automate deception detection with limited intervention of human users. The resulting capabilities show promise for practical use and application by private individuals, investigators, organizations, and others. The need for this research is grounded in the fact that humans are not very effective at detecting deception whether in written or spoken form. Conventional deception detection tools are labour intensive and require extraction of the text in question following ingestion into a deception detection tool, typically utilized after the commission of a crime. Development of real-time deception detection methods provides valuable tools that could serve as proactive deterrents against online malicious activity.

Designed to use a Weblog server, the prototype extracts indicators of deception within user Weblog entries and processes these into numerical values using specialized algorithms. The outputs are then processed by a neural network that learns the difference between truthful and deceptive entries. The neural network can then be used to classify future Weblog entries to determine whether they are deceptive or not. The JACK agents are responsible for synchronizing access to the database, an approach that is considerably easier than using Java to manage access. The agents also manage data ingestion and process the incoming text, transforming it into input vectors required by the neural network. This research demonstrated the effectiveness of combining BDI and neural networks for Information Assurance implementations. It also demonstrated how JACK intelligent agents are very well suited to cyber security tool development.

Acknowledgements

This article is compiled from material submitted by Richard A. Merritts, PhD, CISSP of KRM Consulting LLC (for more information contact: BDIRich@verizon.net), and has been edited to suit the requirements of our research synopses.